Kurdish Music Genre Recognition Using a CNN and DNN

Abstract

Music has different styles, and they are categorized into genres by musicologists. Nonetheless, non-musicologists categorize music differently, such as by finding similarities and patterns in instruments, harmony, and the style of the music. For instance, in addition to popular music genre categorization, such as classic, pop, and modern folkloric, Kurdish music is categorized by Kurdish music lovers according to the type of dance that could go with a particular piece of music. Due to technological advancements, technologies such as artificial intelligence (AI) can help in music genre recognition. Using AI to recognize music genres has been a growing field lately. Computational musicology uses AI in various sectors of studying music. However, the literature shows no evidence of addressing any computational musicology research focusing on Kurdish music. In particular, we have not been able to find any work that indicates the usage of AI in the classification of Kurdish music genres. In this research, we compiled a dataset that comprises 880 samples of 8 Kurdish music genres. We used two machine learning models in our experiments: a convolutional neural network (CNN) and a deep neural network (DNN). According to the evaluations, the CNN model achieved 92% accuracy, while the DNN achieved 90% accuracy. Therefore, we developed an application that uses the CNN model to identify Kurdish music genres by uploading or listening to Kurdish music.

Keywords

• music information retrieval
• music recognition
• music genre classification
• artificial intelligence